Deep learning closure models for large-eddy simulation of flows around bluff bodies
Deep learning closure models for large-eddy simulation of flows around bluff bodies
复制标题
用于钝体周围流动大涡流模拟的深度学习闭合模型
DOI：
10.1017/jfm.2023.446
复制
发表时间：
2023
影响因子：
3.7
通讯作者：
MacArt, Jonathan F.
中科院分区：
文献类型：
--
作者：
Sirignano, Justin;MacArt, Jonathan F.
近壁流动模拟仍然是空气动力学建模中的一个核心挑战：分离流的雷诺平均Navier-Stokes预测通常不准确，并且大涡模拟（LES）可能需要非常小的近壁网格尺寸。通过在控制方程中引入未经训练的神经网络，并针对中等雷诺数下矩形棱柱体周围的不可压缩流动进行原位训练，开发了一种用于LES的深度学习（DL）闭合模型。DL-LES模型使用伴随偏微分方程（PDE）优化方法进行训练，以尽可能接近地匹配直接数值模拟（DNS）数据。然后，他们进行评估的样本外的展弦比，雷诺数和非流线体的几何形状不包括在训练数据，并与标准LES模型进行比较。DL-LES模型优于这些模型，并且能够在相对粗糙的网格上实现准确的LES预测（在每个笛卡尔方向上从DNS网格下采样四或八倍）。我们研究的准确性DL-LES模型预测的阻力系数，近壁和远场平均流，并解决雷诺应力。一个关键的挑战是，感兴趣的LES量是稳态流统计;例如，时间平均速度分量，特别是在相对较短的时间间隔内训练时。我们的研究结果表明，DL-LES模型是准确和稳定的，在很长的时间范围内，这使得稳态的平均速度，波动和阻力系数的非海崖体周围的湍流相关的空气动力学应用的估计。
Near-wall flow simulation remains a central challenge in aerodynamics modelling: Reynolds-averaged Navier–Stokes predictions of separated flows are often inaccurate, and large-eddy simulation (LES) can require prohibitively small near-wall mesh sizes. A deep learning (DL) closure model for LES is developed by introducing untrained neural networks into the governing equations and training in situ for incompressible flows around rectangular prisms at moderate Reynolds numbers. The DL-LES models are trained using adjoint partial differential equation (PDE) optimization methods to match, as closely as possible, direct numerical simulation (DNS) data. They are then evaluated out-of-sample – for aspect ratios, Reynolds numbers and bluff-body geometries not included in the training data – and compared with standard LES models. The DL-LES models outperform these models and are able to achieve accurate LES predictions on a relatively coarse mesh (downsampled from the DNS mesh by factors of four or eight in each Cartesian direction). We study the accuracy of the DL-LES model for predicting the drag coefficient, near-wall and far-field mean flow, and resolved Reynolds stress. A crucial challenge is that the LES quantities of interest are the steady-state flow statistics; for example, a time-averaged velocity component , especially when trained over comparatively short time intervals. Our results demonstrate that the DL-LES models are accurate and stable over long time horizons, which enables the estimation of the steady-state mean velocity, fluctuations and drag coefficient of turbulent flows around bluff bodies relevant to aerodynamics applications.